Driving trajectory planning method, device, electronic device and readable storage medium

Abstract

The present application relates to the field of automotive technology and provides a method, device, electronic device, and readable storage medium for planning a driving trajectory. The method comprises: obtaining a planned trajectory of a target vehicle from a starting point to a destination, wherein the planned trajectory includes a planned path and a planned speed; obtaining target roughness information of the planned path, wherein the target roughness information includes a target roughness area range and a target roughness value corresponding to the target roughness area range; updating the driving position in the planned path according to the target roughness area range and the target roughness value to obtain a target planned path; updating the planned speed corresponding to the target roughness area range according to the target roughness value to obtain a target speed, wherein the target speed is less than the planned speed; obtaining a target driving trajectory of the target vehicle according to the target planned path and the target speed. The present application solves the technical problem of low safety in autonomous driving.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a driving trajectory planning method, device, electronic device, and readable storage medium. Background Art

[0002] In current autonomous driving technology, the smoothness of the road is not taken into consideration when planning the vehicle's trajectory. This results in the vehicle not avoiding potholes in autonomous driving mode, or the vehicle behaving exactly the same on potholes as on flat roads.

[0003] Because the vehicle cannot avoid potholes on the ground or take corresponding actions for potholes when planning its trajectory, it leads to the technical problem of low safety during autonomous driving. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a driving trajectory planning method, device, electronic device and readable storage medium to solve the problem of low safety during automatic driving of vehicles in the prior art.

[0005] A first aspect of an embodiment of the present application provides a method for planning a driving trajectory, comprising:

[0006] Obtain the planned trajectory of the target vehicle from the starting point to the destination. The planned trajectory includes the planned path and planned speed;

[0007] Obtaining target roughness information of the planned path, wherein the target roughness information includes a target roughness area range and a target roughness degree value corresponding to the target roughness area range;

[0008] According to the target uneven area range and the target unevenness value, the driving position in the planned path is updated to obtain the target planned path;

[0009] According to the target roughness value, the planned speed corresponding to the target roughness area is updated to obtain the target speed, where the target speed is less than the planned speed;

[0010] According to the target planning path and target speed, the target driving trajectory of the target vehicle is obtained.

[0011] A second aspect of an embodiment of the present application provides a driving trajectory planning device, comprising:

[0012] The first acquisition module is used to obtain the planned trajectory of the target vehicle from the starting point to the destination, where the planned trajectory includes the planned path and the planned speed;

[0013] A second acquisition module is used to acquire target roughness information of the planned path, wherein the target roughness information includes a target roughness area range and a target roughness degree value corresponding to the target roughness area range;

[0014] A first updating module is used to update the driving position in the planned path according to the target uneven area range and the target unevenness value to obtain a target planned path;

[0015] A second updating module is configured to update a planned speed corresponding to a target unevenness area according to a target unevenness value to obtain a target speed, wherein the target speed is less than the planned speed;

[0016] The third acquisition module obtains the target driving trajectory of the target vehicle according to the target planned path and target speed.

[0017] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0018] According to a fourth aspect of an embodiment of the present application, a readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above method are implemented.

[0019] The beneficial effects of the embodiments of the present application include at least:

[0020] By obtaining the planned trajectory of the target vehicle from the starting point to the destination, the planned trajectory includes the planned path and the planned speed, and the approximate driving route of the current vehicle can be determined, providing identifiable road signs for the subsequent acquisition of target roughness information on the trajectory, so that the planned trajectory can be optimized; by obtaining the target roughness information of the planned path, wherein the target roughness information includes the target roughness area range and the target roughness value corresponding to the target roughness area range, a data basis can be provided for the subsequent optimization of the path and speed, so that the system can identify the uneven areas on the path based on the information, thereby ensuring the stability and safety of the vehicle during automatic driving and improving the user's riding experience; by optimizing the roughness of the planned path based on the target roughness area range and the target roughness value, The driving position is updated to obtain the target planned path, so that when the vehicle finds that there is an uneven area on the path, the path near the uneven area can be corrected and optimized, thereby avoiding potholes on the road, ensuring stability and comfort during automatic driving, and improving driving safety; by updating the planned speed corresponding to the target uneven area according to the target unevenness value, the target speed is obtained, where the target speed is less than the planned speed, so that when the vehicle finds that the uneven area ahead cannot be avoided, it can slow down to a corresponding degree according to the unevenness of the road surface, ensuring the stability of the vehicle's driving and reducing abnormal bumps of the vehicle; finally, based on the target planned path and target speed, the target driving trajectory of the target vehicle is obtained, solving the problem of low safety of current vehicles during automatic driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 This is a flow chart of a driving trajectory planning method provided in an embodiment of the present application;

[0023] Figure 2 This is a flow chart of a method for collecting unevenness information provided in an embodiment of the present application;

[0024] Figure 3 is a schematic diagram of a method for determining the scope of an uneven area provided in an embodiment of the present application;

[0025] Figure 4 This is a schematic diagram of the structure of a driving trajectory planning device provided in an embodiment of the present application;

[0026] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0028] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein. Furthermore, the objects distinguished by "first," "second," and the like are generally of a class, and do not limit the number of objects. For example, the first object may be one or more.

[0029] In addition, it should be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, the elements defined by the phrase "comprises..." do not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the elements.

[0030] A driving trajectory planning method and device according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0031] Figure 1 This is a flow chart of a driving trajectory planning method provided by an embodiment of the present application. This method can be executed by the vehicle side. Figure 1 As shown, the planning method of the driving trajectory includes:

[0032] Step 101: Obtain a planned trajectory of a target vehicle from a starting point to a destination. The planned trajectory includes a planned path and a planned speed.

[0033] Specifically, the planned trajectory can be generated by a lattice planner algorithm, and the best path can be selected from multiple driving paths in the drivable area as the planned path of the planned trajectory; the planned speed can be obtained according to the road speed limit on the path. If there is no speed limit on the road, it can be determined according to the system default value or according to user needs. The speed value can be 30 kilometers per hour, 40 kilometers per hour, 50 kilometers per hour, etc., which is not specifically limited here.

[0034] Specifically, for example, if the user needs to drive the target vehicle from place A to place B, the grid planning algorithm can be used to generate and select the driving trajectory. At this time, the algorithm generates three routes and selects the shortest path as the planned path. The planned speed is then confirmed based on the roads passed on the planned path. Assuming that two roads need to be passed on the path, one has a speed limit of 40 kilometers per hour and the other has no speed limit, the planned speed on both roads can be determined to be 40 kilometers per hour.

[0035] By planning the trajectory from the starting point to the destination, the target vehicle can determine the approximate route it currently needs to travel, providing a preliminary, relatively rough driving path and driving speed. At the same time, by determining the roads on the driving path, identifiable road signs can be provided for subsequent acquisition of target roughness information on the trajectory, thereby optimizing the planned trajectory.

[0036] Step 102 : obtaining target roughness information of the planned path, wherein the target roughness information includes a target roughness area range and a target roughness degree value corresponding to the target roughness area range.

[0037] Specifically, after determining the vehicle's current planned trajectory, the path to be traveled can be detected to determine the road information contained on the path. Based on this road information, target roughness information can be collected from a preset database to determine the location, range and roughness value of the uneven sections on these roads, providing a data basis for optimizing the previously planned path and planned speed based on the roughness information. This allows the system to identify uneven areas on the path based on this information, ensuring the stability and safety of the vehicle during automatic driving and improving the user's riding experience.

[0038] Step 103 : updating the driving position in the planned path according to the target uneven area range and the target unevenness value to obtain the target planned path.

[0039] Specifically, the magnitude of the roughness value determines whether the vehicle needs to avoid the uneven area, and thus whether the planned path needs to be modified and optimized. The extent of the uneven area determines the distance the vehicle needs to detour to avoid the uneven area, and thus determines the length of the planned path that needs to be optimized. After the planned path for each uneven area is updated and optimized, the target planned path is obtained.

[0040] By using the range and degree of unevenness of the uneven area to obtain the target planned path, the vehicle can correct and optimize the path near the uneven area when it finds that there is an uneven area on the path, thereby avoiding potholes on the road, ensuring stability and comfort during autonomous driving, and improving driving safety.

[0041] Step 104 : updating the planned speed corresponding to the target unevenness area according to the target unevenness value to obtain a target speed, wherein the target speed is less than the planned speed.

[0042] Specifically, when an uneven section of the road ahead is found that cannot be avoided by correcting the path, the vehicle's speed on the uneven section can be corrected to ensure smooth driving. Specifically, the vehicle can be decelerated at a certain distance from the uneven section to ensure that the vehicle does not enter the uneven section and then decelerate, resulting in excessive vehicle bumps. This improves the safety and comfort of the vehicle's automatic driving on potholes. In addition, when optimizing the speed based on the unevenness value, the planned speed can also be preliminarily optimized, mainly to reduce the vehicle's acceleration and the rate of change of acceleration jerk, thereby improving the smoothness and comfort of autonomous driving.

[0043] Step 105 , obtaining a target driving trajectory of the target vehicle according to the target planned path and target speed.

[0044] By combining the target planned path and target speed to obtain the target driving trajectory, the vehicle's driving trajectory during autonomous driving can avoid uneven sections of the road as much as possible. When uneven sections cannot be avoided, the vehicle can also perform corresponding deceleration operations in advance based on the unevenness value of the uneven section ahead, ensuring the stability and safety of the vehicle during autonomous driving and improving the passenger experience.

[0045] According to the technical solution provided by the embodiment of the present application, by obtaining the planned trajectory of the target vehicle from the starting point to the destination, the planned trajectory includes the planned path and the planned speed, the approximate driving route of the current vehicle can be determined, and a recognizable road sign is provided for the subsequent acquisition of the target roughness information on the trajectory, so that the planned trajectory can be optimized. By obtaining the target roughness information of the planned path, wherein the target roughness information includes the target roughness area range and the target roughness value corresponding to the target roughness area range, a data basis can be provided for the subsequent optimization of the path and speed, so that the system can identify the uneven areas on the path based on the information, thereby ensuring the smoothness and safety of the vehicle during automatic driving and improving the user's riding experience. By updating the driving position in the planned path according to the target roughness area range and the target roughness value, the target planned path is obtained, so that when the vehicle finds that there is an uneven area on the path, it can correct and optimize the path near the uneven area, thereby avoiding potholes, ensuring the smoothness and comfort during automatic driving, and improving the safety during driving. By updating the planned speed corresponding to the target unevenness area based on the target unevenness value, the target speed is obtained. The target speed is lower than the planned speed. This allows the vehicle to decelerate accordingly to the road's unevenness when it finds an unavoidable uneven area ahead, ensuring smooth driving and reducing abnormal vehicle jolting. Finally, based on the target planned path and target speed, the target vehicle's target trajectory is obtained, addressing the current safety issues associated with autonomous driving.

[0046] In some embodiments, the target roughness information further includes first road sign information; and obtaining the target roughness information of the planned path includes:

[0047] Obtain second road identification information of a road corresponding to the planned path; based on the second road identification information, filter out first road identification information that matches the second road identification information from a preset database, and determine the road roughness information corresponding to the first road identification information as the target roughness information of the planned path; wherein the preset database includes road roughness information of multiple roads, including the road corresponding to the planned path.

[0048] Specifically, the first and second road sign information can include road names, road numbers, and landmarks around the road. These can be used to identify the current road location and characteristics. First, the vehicle's planned route is used to determine the roads it will traverse. Once these roads are determined, the second road sign information for these roads is obtained. Next, the first road sign information for roads in a preset database is extracted and compared with the second road sign information. If a match is found, the road roughness information corresponding to the matched first road sign information is used as the target roughness information.

[0049] In addition, specifically, a large amount of road roughness information contained in the preset database is obtained by uploading road roughness information collected by multiple vehicles during road driving to the cloud, and then the cloud integrates and optimizes the road information to finally obtain the preset database.

[0050] This implementation utilizes road sign information to search for target roughness information corresponding to the planned path from a preset database, making the search for target roughness information faster and more accurate. This avoids situations where the vehicle does not take any action when it needs to slow down or avoid uneven road sections due to erroneous road information found in the database, thereby improving safety and comfort during autonomous driving.

[0051] In some embodiments, further comprising:

[0052] When a target vehicle is detected traveling on any road, driving data of the target vehicle is acquired through an inertial measurement unit, wherein the driving data includes a current driving speed, a first angular velocity about a longitudinal axis, and a second angular velocity about a horizontal axis;

[0053] Based on the driving data, a current roughness value of the current position on the road is obtained; if the current roughness value is greater than or equal to a preset value, a current roughness area corresponding to the current roughness value is obtained based on the body length of the target vehicle and the detection result of whether the road has lane lines;

[0054] The road sign information of the road is obtained through the on-board navigation system of the target vehicle; the current roughness value of the road, the current roughness area range and the road sign information are combined to obtain the current roughness information of the road, and the current roughness information of the road is uploaded to a preset database.

[0055] Specifically, for any road, when a target vehicle is traveling on that road, the system continuously monitors the vehicle through an inertial measurement unit (IMU) and continuously acquires current vehicle driving data. This driving data includes driving speed, a first angular velocity about the longitudinal axis, and a second angular velocity about the horizontal axis. Specifically, when the vehicle is traveling on a horizontal surface, the direction of the vehicle's front can be set as the direction of the X-axis (i.e., the longitudinal axis), and the direction of the vehicle's left side can be set as the direction of the Y-axis (i.e., the horizontal axis). The center point of the vehicle is used as the origin, forming a rectangular coordinate system. When traveling on a horizontal surface, the first and second angular velocities of the vehicle are both zero. If there is a depression in the left front of the vehicle, the vehicle will tilt toward the left front, and the first and second angular velocities are both positive. If there is a bump in the left front of the vehicle, the vehicle will tilt toward the right rear, and the first and second angular velocities are both negative. Therefore, the first angular velocity about the longitudinal axis and the second angular velocity about the horizontal axis can be used to determine and calculate the roughness of the uneven road section encountered by the vehicle.

[0056] When the calculated roughness value at the vehicle's current location is less than a preset value, the roughness value at that location can be assigned a value of 0, and the area at that location can be determined as a flat area. This is because if the roughness value is less than the preset value, it may be due to the presence of small, movable debris or obstacles such as stones or water bottles in the area, which can be ignored. It should be noted that the preset value can be a fixed value, can be set based on vehicle performance, or can be set based on the average performance of most vehicles, and this is not limited here.

[0057] When the roughness value at the vehicle's current location is calculated to be greater than or equal to a preset value, the roughness value at that location is recorded and identified as an uneven area. The scope of the uneven area is then determined based on factors such as vehicle length. The road marking information for that section is then obtained, and finally the scope of the uneven area, the roughness value, and the marking information are combined and uploaded to a preset database.

[0058] This implementation continuously calculates the roughness value of the road traveled during driving, confirms the scope of the uneven area based on factors such as the vehicle body length, and finally obtains the roughness information of the current road and stores it in a preset database. When other vehicles subsequently travel to this road section, they can obtain this information from the preset database, without having to travel the entire way to obtain the road roughness information. This realizes data sharing of road roughness information between vehicles, improves the efficiency of vehicles in obtaining road roughness information, and improves the comfort and safety during autonomous driving.

[0059] In some embodiments, obtaining a current roughness value of a current position on the road based on driving data includes:

[0060] Based on the driving data, the current roughness value of the current location is calculated using the following formula:

[0061]

[0062] Among them, U represents the current roughness value of the current position, ω x represents the first angular velocity, ω y represents the second angular velocity, and v represents the current driving speed.

[0063] This embodiment calculates the current roughness value using the above formula. This road roughness calculation method, utilizing the vehicle's IMU and positioning system, improves the accuracy of road roughness detection with ease and provides more information for the autonomous driving decision-making and planning module. Furthermore, the road roughness is quantified and displayed numerically, making it more intuitive and simple for users to understand the current road roughness.

[0064] In some embodiments, based on the body length of the target vehicle and the detection result of whether the road has lane lines, the current uneven area range corresponding to the current unevenness value is obtained, including:

[0065] If the detection result indicates that there is a lane line on the road, the lane width of the target vehicle is obtained, the lane width is determined as the width of the current uneven area range, and the vehicle body length is determined as the length of the current uneven area range, thereby obtaining the current uneven area range;

[0066] When the detection result indicates that there is no lane line on the road, the preset width is determined as the width of the current uneven area range, and the vehicle body length is determined as the length of the current uneven area range to obtain the current uneven area range; the preset width is the sum of the vehicle body width of the target vehicle and the vehicle body width of the preset ratio.

[0067] Specifically, the overall process of determining the current uneven area can be as follows: Figure 2 As shown, first, the driving data of the current vehicle is obtained, and the current roughness value of the road is calculated using the driving data and the calculation formula of the current roughness value to obtain the current roughness value U.

[0068] The current roughness value can then be compared with the preset value. When the current roughness value is less than the preset value, the current roughness value of the road section can be set to 0; when the current roughness value is greater than or equal to the preset value, it indicates that the road section is an uneven area and the scope of the uneven area needs to be determined. At this time, the vehicle's body length will be obtained and the surrounding environment will be sensed. When lane lines are detected around the vehicle, the current lane will be associated with the current roughness value, that is, the width of the lane the vehicle is currently in will be used as the width of the current uneven area, and the vehicle's body length will be used as the length of the current uneven area, thereby obtaining the scope of the current uneven area. The specific judgment can be as follows: Figure 3 As shown, the width of lane 2 is the width of the current uneven area. When no lane lines are detected around the vehicle, the preset width is associated with the current unevenness value, that is, the preset width is used as the width of the current uneven area, and the vehicle body length is used as the length of the current uneven area, thereby obtaining the range of the uneven area. The preset width can be 1.5 times the vehicle width, 2.0 times the vehicle width, etc. Specifically, it can be centered on the vehicle origin and widened to 0.75 times the vehicle width on both sides as the preset width.

[0069] This embodiment determines the scope of the current uneven area by detecting lane lines, so that the scope of the current uneven area can be determined regardless of whether there are identifiable lane lines around the vehicle. In addition, using this determination method, the area scope can be determined more simply and quickly, greatly saving measurement and determination time and improving the efficiency of collecting unevenness information.

[0070] In some embodiments, updating the driving position in the planned path according to the target uneven area range and the target unevenness value to obtain the target planned path includes:

[0071] The product of the target roughness value and the first preset weight value is calculated to obtain a target roughness cost corresponding to the target roughness area range; the preset path planning algorithm is adjusted according to the target roughness cost, and the driving position in the planned path is updated according to the adjusted path planning algorithm to obtain a target planned path; wherein the target planned path does not include the target roughness area range.

[0072] Specifically, when an uneven road section that needs to be avoided is found on the planned path, a detour can be used to ensure safety and comfort during autonomous driving. The path planning algorithm can be adjusted based on the target roughness cost, which can be calculated using the following formula:

[0073] J road =ω road ·U

[0074] Among them, J road It can be expressed as the target roughness cost, ω road It can be expressed as a first preset weight value, and U can be expressed as the roughness value of the uneven road section ahead; the first preset weight value can be determined according to actual conditions or system defaults, and the larger the first preset weight value, the greater the penalty for the vehicle driving through the uneven road area.

[0075] In addition to the target roughness cost mentioned above, the constraints of the path planning algorithm may also include: the vehicle's driving trajectory must be within the drivable area; the vehicle cannot collide with obstacles during driving; the curvature of the vehicle's driving trajectory cannot exceed the vehicle's own turning limit; and the vehicle's driving trajectory must be continuous and cannot be interrupted in the middle. Costs may include: the vehicle's own trajectory needs to be as close to the reference line as possible, that is, the vehicle's own centerline; the vehicle's own lateral speed needs to be as small as possible; the vehicle's own lateral acceleration needs to be as small as possible; and the rate of change of the vehicle's own lateral acceleration also needs to be as small as possible. Only in this way can the vehicle's driving trajectory generated by the path planning algorithm be able to avoid severely potholes as much as possible.

[0076] This embodiment adds a target roughness cost to the path planning algorithm to ensure that the generated path can avoid potholes as much as possible, thereby improving the safety and comfort of autonomous driving vehicles.

[0077] In some embodiments, the planned speed corresponding to the target unevenness area range is updated according to the target unevenness value to obtain the target speed, including:

[0078] The product of the target roughness value and the second preset weight value is calculated; the difference between the planned speed corresponding to the target roughness area range and the product is calculated, and the difference is determined as the target speed.

[0079] Specifically, when an unavoidable uneven road section is found on the planned path, deceleration can be used to ensure safety and comfort during autonomous driving. The degree of deceleration can be determined based on the degree of road unevenness. The specific process can be implemented using the following formula:

[0080] v=v origin -U·k

[0081] Among them, v can be expressed as the target speed of the target vehicle, v origin_{\text{k}} may represent the planned speed of the target vehicle, U may represent the roughness of the uneven road ahead, and k may represent a second preset weight value. The second preset weight value may be obtained through repeated experiments or determined by system default or user needs. The second preset weight value may be 100, 120, 140, etc., and is not specifically limited here.

[0082] Specifically, for example, when a vehicle is planning a target path and finds an uneven area on the road that cannot be avoided by changing the path, it can choose to reduce the impact of the uneven area ahead on the vehicle by slowing down, thereby ensuring the smooth driving of the vehicle to the greatest extent. At this time, the current unevenness value can be determined as 0.1, the second preset weight value is 120, and the planned speed is 40 kilometers per hour. At this time, the above formula can be used to calculate the target speed, that is, 40-0.1×120=28, so the target speed when passing through the uneven road section should be 28 kilometers per hour.

[0083] This embodiment updates and optimizes the planned speed by utilizing preset weights and roughness values. This allows the vehicle to decelerate accordingly based on the severity of the potholes when it is unable to avoid them. This reduces the vehicle's turbulence and improves the safety and comfort of autonomous driving on pothole-prone roads.

[0084] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0085] All of the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described in detail here.

[0086] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0087] Figure 4 This is a schematic diagram of a driving trajectory planning device provided in an embodiment of the present application. Figure 4 As shown, the device includes:

[0088] The first acquisition module 401 is used to obtain the planned trajectory of the target vehicle from the starting point to the destination, where the planned trajectory includes the planned path and the planned speed;

[0089] The second acquisition module 402 is configured to acquire target roughness information of the planned path, wherein the target roughness information includes a target roughness area range and a target roughness value corresponding to the target roughness area range;

[0090] The first updating module 403 is used to update the driving position in the planned path according to the range of the uneven area and the unevenness value to obtain a target planned path;

[0091] The second updating module 404 is configured to update the planned speed corresponding to the target unevenness area according to the target unevenness value to obtain a target speed, wherein the target speed is less than the planned speed;

[0092] The third acquisition module 405 is used to obtain a target driving trajectory of the target vehicle according to the target planned path and target speed.

[0093] According to the technical solution provided in the embodiment of the present application, the planned trajectory of the target vehicle from the starting point to the destination is obtained by the first acquisition module 401. The planned trajectory includes the planned path and the planned speed. The approximate driving route of the current vehicle can be determined, and a recognizable road sign is provided for the subsequent acquisition of the target roughness information on the trajectory, so that the planned trajectory can be optimized. The target roughness information of the planned path is obtained by the second acquisition module 402, wherein the target roughness information includes the target roughness area range and the target roughness value corresponding to the target roughness area range, which can provide a data basis for the subsequent optimization of the path and speed, so that the system can identify the uneven area on the path based on the information, ensuring the smoothness and safety of the vehicle during automatic driving, and improving the user's riding experience. The first update module 403 updates the driving position in the planned path according to the target roughness area range and the target roughness value to obtain the target planned path, so that the vehicle can correct and optimize the path near the uneven area when it finds that there is an uneven area on the path, thereby avoiding potholes, ensuring the smoothness and comfort during automatic driving, and improving the safety during driving. The second updating module 404 updates the planned speed corresponding to the target unevenness area based on the target unevenness value to obtain a target speed. The target speed is lower than the planned speed. This allows the vehicle to decelerate according to the road's unevenness when it detects an unavoidable uneven area ahead, ensuring smooth driving and reducing abnormal jolting. Finally, the third acquisition module 405 obtains the target vehicle's target trajectory based on the target planned path and target speed, addressing the current safety issues associated with autonomous driving.

[0094] In some embodiments, the target roughness information also includes first road identification information; the second acquisition module 402 is specifically used to: obtain second road identification information of the road corresponding to the planned path; based on the second road identification information, filter out first road identification information that matches the second road identification information from a preset database, and determine the road roughness information corresponding to the first road identification information as the target roughness information of the planned path; wherein the preset database includes road roughness information of multiple roads, and the roads include the road corresponding to the planned path.

[0095] In some embodiments, the second acquisition module 402 is also used to: when a target vehicle is detected traveling on any road, obtain driving data of the target vehicle through an inertial measurement unit, wherein the driving data includes a current driving speed, a first angular velocity around a longitudinal axis, and a second angular velocity around a horizontal axis; obtain a current roughness value of a current position in the road according to the driving data; when the current roughness value is greater than or equal to a preset value, obtain a current roughness area range corresponding to the current roughness value according to the detection results of the body length of the target vehicle and whether the road has a lane line; obtain road sign information of the road through the on-board navigation system of the target vehicle; combine the current roughness value, the current roughness area range, and the road sign information of the road to obtain current roughness information of the road, and upload the current roughness information of the road to a preset database.

[0096] In some embodiments, the second acquisition module 402 is further configured to calculate the current roughness value of the current location according to the driving data using the following formula:

[0097]

[0098] Among them, U represents the current roughness value of the current position, ω x represents the first angular velocity, ω y represents the second angular velocity, and v represents the current driving speed.

[0099] In some embodiments, the second acquisition module 402 is also used to: when the detection result indicates that there is a lane line on the road, obtain the lane width of the lane where the target vehicle is located, determine the lane width as the width of the current uneven area range, and determine the vehicle body length as the length of the current uneven area range, to obtain the current uneven area range; when the detection result indicates that there is no lane line on the road, determine the preset width as the width of the current uneven area range, and determine the vehicle body length as the length of the current uneven area range, to obtain the current uneven area range; wherein the preset width is the sum of the vehicle body width of the target vehicle and the vehicle body width of the preset proportion.

[0100] In some embodiments, the first update module 403 is specifically used to: calculate the product of the target roughness value and the first preset weight value to obtain the target roughness cost corresponding to the target uneven area range; adjust the preset path planning algorithm according to the target roughness cost, and update the driving position in the planned path according to the adjusted path planning algorithm to obtain the target planned path; wherein, the target planned path does not include the uneven area range.

[0101] In some embodiments, the second updating module 404 is specifically configured to: calculate the product of the target roughness value and the second preset weight value; calculate the difference between the planned speed corresponding to the target roughness area range and the product, and determine the difference as the target speed.

[0102] It should be noted that the device provided in this application can implement all the method steps executed by the above method and can achieve the same technical effect, which will not be repeated here.

[0103] Figure 5 Schematic diagram of the electronic device 5 provided in the embodiment of the present application. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable by the processor 501. When the processor 501 executes the computer program 503, the steps of the above-mentioned method embodiments are implemented. Alternatively, when the processor 501 executes the computer program 503, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0104] The electronic device 5 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 5 may include but is not limited to a processor 501 and a memory 502. Those skilled in the art will appreciate that Figure 5 This is merely an example of the electronic device 5 and does not limit the electronic device 5 . The electronic device 5 may include more or fewer components than shown in the figure, or different components.

[0105] The processor 501 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0106] The memory 502 can be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 502 can also be an external storage device of the electronic device 5, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. The memory 502 can also include both an internal storage unit of the electronic device 5 and an external storage device. The memory 502 is used to store computer programs and other programs and data required by the electronic device.

[0107] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0108] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the present application implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, which may be in source code form, object code form, executable file or some intermediate form. The readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0109] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A driving trajectory planning method, characterized in that: include: Obtaining a planned trajectory of a target vehicle from a starting point to a destination, wherein the planned trajectory includes a planned path and a planned speed; Obtaining target roughness information of the planned path, wherein the target roughness information includes a target roughness area range and a target roughness value corresponding to the target roughness area range; when the target vehicle is traveling on any road, a current roughness value of a current position on the road is obtained based on a current traveling speed of the target vehicle, a first angular velocity about a longitudinal axis, and a second angular velocity about a horizontal axis; updating the driving position in the planned path according to the target uneven area range and the target unevenness value to obtain a target planned path; updating the planned speed corresponding to the target unevenness area according to the target unevenness value to obtain a target speed, wherein the target speed is less than the planned speed; Obtaining a target driving trajectory of the target vehicle according to the target planned path and the target speed; updating the driving position in the planned path according to the target unevenness area range and the target unevenness value to obtain a target planned path, including: calculating the product of the target unevenness value and a first preset weight to obtain a target unevenness cost corresponding to the target unevenness area range; adjusting a preset path planning algorithm according to the target unevenness cost, and updating the driving position in the planned path according to the adjusted path planning algorithm to obtain a target planned path; wherein the target planned path does not include the target unevenness area range; The method includes updating the planned speed corresponding to the target unevenness area range according to the target unevenness value to obtain the target speed, including: calculating the product of the target unevenness value and a second preset weight value; calculating the difference between the planned speed corresponding to the target unevenness area range and the product, and determining the difference as the target speed.

2. The method according to claim 1, characterized in that The target roughness information also includes first road sign information; The obtaining target roughness information of the planned path includes: Acquire second road identification information of the road corresponding to the planned path; According to the second road sign information, first road sign information matching the second road sign information is screened from a preset database, and road roughness information corresponding to the first road sign information is determined as target roughness information of the planned path; The preset database includes road roughness information of a plurality of roads, and the roads include the roads corresponding to the planned path.

3. The method according to claim 1 or 2, characterized in that Also includes: When the target vehicle is detected traveling on any road, driving data of the target vehicle is acquired through an inertial measurement unit, wherein the driving data includes a current driving speed, a first angular velocity about a longitudinal axis, and a second angular velocity about a horizontal axis; obtaining a current roughness value of a current position on the road according to the driving data; When the current roughness value is greater than or equal to a preset value, obtaining a current roughness area range corresponding to the current roughness value according to a body length of the target vehicle and a detection result of whether the road has lane lines; Obtaining road sign information of the road through the onboard navigation system of the target vehicle; The current roughness value of the road, the current roughness area range and the road sign information are combined to obtain current roughness information of the road, and the current roughness information of the road is uploaded to a preset database.

4. The method according to claim 3, characterized in that Obtaining a current roughness value of a current position on the road according to the driving data includes: According to the driving data, the current roughness value of the current position is calculated using the following formula: in, represents the current roughness value of the current position, represents the first angular velocity, represents the second angular velocity, Indicates the current driving speed.

5. The method according to claim 3, characterized in that The obtaining of a current unevenness area range corresponding to the current unevenness value based on the detection result of the body length of the target vehicle and whether the road has a lane line includes: If the detection result indicates that a lane line exists on the road, obtaining a lane width of the lane where the target vehicle is located, determining the lane width as the width of the current uneven area range, and determining the vehicle body length as the length of the current uneven area range, to obtain the current uneven area range; When the detection result indicates that no lane line exists on the road, a preset width is determined as the width of the current uneven area range, and the vehicle body length is determined as the length of the current uneven area range to obtain the current uneven area range; wherein the preset width is the sum of the vehicle body width of the target vehicle and the vehicle body width of a preset ratio.

6. A driving trajectory planning device, characterized in that: include: A first acquisition module is used to obtain a planned trajectory of the target vehicle from a starting point to a destination, wherein the planned trajectory includes a planned path and a planned speed; a second acquisition module configured to acquire target roughness information of the planned path, wherein the target roughness information includes a target roughness area range and a target roughness value corresponding to the target roughness area range; and when the target vehicle is traveling on any road, a current roughness value of a current position on the road is obtained based on a current traveling speed of the target vehicle, a first angular velocity about a longitudinal axis, and a second angular velocity about a horizontal axis; A first updating module is configured to update the driving position in the planned path according to the target uneven area range and the target unevenness value to obtain a target planned path; A second updating module is configured to update the planned speed corresponding to the target unevenness area according to the target unevenness value to obtain a target speed, wherein the target speed is less than the planned speed; A third acquisition module is used to obtain a target driving trajectory of the target vehicle according to the target planned path and the target speed; The first updating module is specifically configured to calculate the product of the target roughness value and a first preset weight to obtain a target roughness cost corresponding to the target roughness area range; adjust a preset path planning algorithm according to the target roughness cost, and update the driving position in the planned path according to the adjusted path planning algorithm to obtain a target planned path; wherein the target planned path does not include the target roughness area range; The second updating module is specifically configured to calculate the product of the target roughness value and a second preset weight value; calculate the difference between the planned speed corresponding to the target roughness area range and the product, and determine the difference as the target speed.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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